Measuring inequities in transportation injuries in a Canadian commuter cohort: impacts of individual versus neighbourhood income
Bibliographic record
Abstract
Abstract Background Low income has been associated with a higher risk of transportation-related injury however, previous studies have largely relied on area-level income, due to the limited availability of individual-level data. Methods To examine the independent and combined roles of individual- and area-level income, this prospective cohort study followed ~ 6,557,000 Canadians from the Canadian Census Health and Environment Cohorts (2006, 2011, 2016), for pedestrian, bicycling, or motor vehicle hospitalizations. Income was measured (1) individually by the low-income cut-off and (2) at the area level using neighbourhood income quintiles. Poisson regression estimated the incidence rate ratios (IRR) and 95% confidence intervals (CI) for transportation-related hospitalizations. Results After adjusting for covariates, low-income individuals had higher risks of hospitalizations for pedestrian (IRR = 1.93, 95%CI (1.62, 2.29)), bicycling (IRR = 1.16, 95%CI (1.01, 1.34)) and motor vehicle injuries (IRR = 1.18, 95%CI (1.06, 1.31)). When both individual and neighbourhood income were assessed together we estimated, that those who lived in the lowest income neighbourhoods (compared to the highest) had a higher risk of pedestrian (IRR = 1.80, 95%CI (1.51, 2.14)) and motor vehicle injury (IRR = 1.33, 95%CI (1.22, 1.42)) but lower risk of bicycling injury (IRR = 0.73, 95%CI (0.65, 0.81)). Conclusions The interaction between individual and neighbourhood income revealed an increased injury risk for low-income individuals in all neighbourhoods, with large inequities in pedestrian and motor vehicle injury risk persisting even in the highest-income neighbourhoods. These findings demonstrate individual income independently contributes to transportation injury risk, underscoring the importance of considering both individual- and area-level income.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".